Since our approach learns cluster centroids using RL, it is related to the popular cluster learning strategy for classification called Vector of Locally Aggregated Descriptors (VLAD) [1]}. The more recent NetVLAD [1]} leverages neural networks which helps outperform the standard VLAD by a wide margin. ActionVLAD [3]} aggregates NetVLAD over time to obtain descriptors for videos. ActionVLAD uses clusters that correspond to spatial locations in a video while we use joint visual semantic embeddings for the entire video. In general, VLAD uses residuals
with respect to cluster centroids as representation while CLASTER uses a weighting of the centroids. The proposed CLASTER outperforms NetVLAD by a large margin on both HMDB51 and UCF101.
